mask_the_face.py

# Author: aqeelanwar
# Created: 27 April,2020, 10:22 PM
# Email: [email protected]

import argparse
import dlib
from aux_functions import *
import numpy as np

def maskThisImages(myImg):

    # Command-line input setup
    parser = argparse.ArgumentParser(
        description="MaskTheFace - Python code to mask faces dataset"
    )
    parser.add_argument(
        "--path",
        type=str,
        default="",
        help="Path to either the folder containing images or the image itself",
    )
    parser.add_argument(
        "--mask_type",
        type=str,
        default="surgical",
        choices=["surgical", "N95", "KN95", "cloth", "gas", "inpaint", "random", "all"],
        help="Type of the mask to be applied. Available options: all, surgical_blue, surgical_green, N95, cloth",
    )

    parser.add_argument(
        "--pattern",
        type=str,
        default="",
        help="Type of the pattern. Available options in masks/textures",
    )

    parser.add_argument(
        "--pattern_weight",
        type=float,
        default=0.5,
        help="Weight of the pattern. Must be between 0 and 1",
    )

    parser.add_argument(
        "--color",
        type=str,
        default="#0473e2",
        help="Hex color value that need to be overlayed to the mask",
    )

    parser.add_argument(
        "--color_weight",
        type=float,
        default=0.5,
        help="Weight of the color intensity. Must be between 0 and 1",
    )

    parser.add_argument(
        "--code",
        type=str,
        # default="cloth-masks/textures/check/check_4.jpg, cloth-#e54294, cloth-#ff0000, cloth, cloth-masks/textures/others/heart_1.png, cloth-masks/textures/fruits/pineapple.png, N95, surgical_blue, surgical_green",
        default="",
        help="Generate specific formats",
    )


    parser.add_argument(
        "--verbose", dest="verbose", action="store_true", help="Turn verbosity on"
    )
    parser.add_argument(
        "--write_original_image",
        dest="write_original_image",
        action="store_true",
        help="If true, original image is also stored in the masked folder",
    )
    parser.set_defaults(feature=False)

    args, unknown = parser.parse_known_args()
    args.write_path = args.path + "_masked"

    # Set up dlib face detector and predictor
    args.detector = dlib.get_frontal_face_detector()
    path_to_dlib_model = "shape_predictor_68_face_landmarks.dat"
    if not os.path.exists(path_to_dlib_model):
        download_dlib_model()

    args.predictor = dlib.shape_predictor(path_to_dlib_model)

    # Extract data from code
    mask_code = "".join(args.code.split()).split(",")
    args.code_count = np.zeros(len(mask_code))
    args.mask_dict_of_dict = {}


    for i, entry in enumerate(mask_code):
        print
        mask_dict = {}
        mask_color = ""
        mask_texture = ""
        mask_type = entry.split("-")[0]
        if len(entry.split("-")) == 2:
            mask_variation = entry.split("-")[1]
            if "#" in mask_variation:
                mask_color = mask_variation
            else:
                mask_texture = mask_variation
        mask_dict["type"] = mask_type
        mask_dict["color"] = mask_color
        mask_dict["texture"] = mask_texture
        args.mask_dict_of_dict[i] = mask_dict

    # Check if path is file or directory or none
    is_file=True
    # Process if the path was a file
    if is_file:
        print("Masking image file")
        image_path = args.path
        write_path = args.path.rsplit(".")[0]
        if True:
            # Proceed if file is image
            # masked_images, mask, mask_binary_array, original_image
            masked_image, mask, mask_binary_array, original_image = mask_image(
                myImg, args
            )
            if len(masked_image)==0:
                return masked_image
            else:
                img = masked_image[i]
                return img
    else:
        print("Path is neither a valid file or a valid directory")
    print("Processing Done")